BREAKING! COVID-19-News: IBM Study Shows Co-Infections With Various SARS-CoV-2 Lineages Becoming Common! Heteroplasmy More Prevalent Than Thought!
: Researchers from IBM Research at T.J. Watson Research Center-New York, have in a new study found that heteroplasmy was more common in COVID-19 patients that actually thought. Heteroplasmy is a condition when a COVID-19 patient is co-infected with more than one variant or lineage of the SARS-CoV-2 coronavirus. The implications from this study finding are massive in terms of treatments, understanding the variety of clinical manifestations in different patients and why different individuals have different immune responses. The findings also reflect the current inadequate importance and emphasis of genomic sequencings, studies and trackings (deliberate or not!) by health agencies and authorities.
The study findings were published on a preprint server but are currently being peer reviewed. https://www.biorxiv.org/content/10.1101/2020.10.14.339986v1.full.pdf
IBM has been working proactively in the medical and healthcare sector creating a multitude of computing and AI platforms to help assist in diagnostics, genes studies and even in therapeutics and drug development.
The company has been playing a very active role in the COVID-19 crisis in trying to provide researchers with tools to help expedite studies etc. One such platform is the Concerti algorithm for understanding evolutionary phylogenies
Although extensive sequencing efforts are ongoing to understand this virus's evolution, several studies with SARS-CoV-2 sequencing data show different allele frequencies of the virus in the same patient, a phenomenon called heteroplasmy
The study team presented a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer 19 patient. The commonality is in the phylogenetic retrodiction based on the genomic reads in both scenarios. While there is evidence of heteroplasmy, i.e., multiple lineages of SARS-CoV-2 in the same COVID-19 patient; to date, there is no evidence of sublineages recombining within the same patient. The heterogeneity in a patient’s tumor is analogous to intra-patient heteroplasmy and the absence of recombination in the cells of tumor is a widely accepted assumption. Just as the different frequencies of the genomic variants in a tumor presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, the team postulates that so do the different variant frequencies in the viral reads, offering the means to infer the multiple co-infecting sublineages. The team describes the Concerti computational framework for inferring phylogenies in each of the two scenarios. To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. The team also made some additional discoveries. They uncovered new potential parallel mutation in the evolution of the SARS-CoV-2 virus.
According to the study team the most probable explanation for this intra-patient heterogenic viral reads is the existence of multiple viral strains.
It is also noted that recombination is an unlikely explanation because the chances of the virus being functional after disassembly inside the host cell and reassembly into
a virion having a different sequence are pretty low. While there is evidence of multiple lineages of SARS-CoV-2 virus in the same COVID-19 patient, no evidence of sublineages recombining in the same patient is available to date.
Importantly multiple strains of a virus infecting the same patient have huge clinical implications in epidemiology, treatment, and controlling the pandemic.
Variations in viral strains can indicate different transmissibility levels, different drug resistance mechanisms, varying responses to treatment, and explain the wide variety of symptomology. Given the significance of this in treatment and vaccine development, it is imperative that the more research focuses on heteroplasmy of SARS-CoV-2.
Experts and researchers from the IBM Research, T.J. Watson Research Center, NY, USA, recently presented a common methodological framework to interpret the phylogenomics from genomic data for multiple diseases, including COVID-19 and cancer.
For example in the case of cancer, the tumor heterogeneity in a patient indicates intra-patient heteroplasmy, and the absence of recombination in tumor cells is an accepted assumption.
The study team hypothesize that just like the different frequencies of the genomic variants of a tumor indicates multiple tumor clones and offers a handle to infer them computationally, the different variant frequencies in viral genomic reads offer the means to compute the multiple co-infecting sublineages.
The new research describes a computational framework called Concerti to infer phylogenies in both the above scenarios. To demonstrate the accuracy of this algorithm, the researchers reproduced some previously known results in both scenarios. They also identified a novel potential parallel mutation in the SARS-CoV-2 virus and uncovered new clones having therapy-resistant mutations in the context of cancer.
Concerti's ability to extract and integrate information from multiple points, sites, times, or samples makes it possible to discover phylogenetic trees that capture the spatial and temporal heterogeneity. These phylogeny models can directly impact therapeutics as they can highlight the "birth" of clones that may harbor mechanisms of treatment resistance, "death" of subclones with drug targets, and the acquisition of functionally relevant mutations in clones that may seem clinically irrelevant.
The study team demonstrated how Concerti could be applied to any genomic sequencing dataset with different allele frequencies, be it cancer or SARS-CoV-2, and how the results provided by the algorithm can have significant disease-specific clinical implications.
The study team said, "We demonstrate in this paper how Concerti can be applied to any genomic sequencing dataset with varying allele frequencies, whether it be cancer or the new SARS-CoV-2 virus causing the COVID-19 pandemic, and the results can have profound disease-specific clinical implication."
Importantly specific integration of multi-point data could improve treatment response.
The identifying the presence of many viral strains in a single host can profoundly impact treatment approaches, vaccine development efforts, and infection mitigation strategies.
The Concerti data for COVID-19 patients shows the ability to identify viral strains based on different allele frequencies and thus discover the presence of new homoplasies. The researchers believe that the results provided by Concerti effectively addresses crucial challenges faced by researches in the development of therapeutics and vaccines.
Just as with cancer, accurate monitoring of tumor evolution over the disease course can help identify new drug targets and therapeutic methods that could stabilize this disease and manage the pressures of treatment exposure and tumor environment changes.
The research findings highlight how specific integration of multi-point data by Concerti could facilitate more optimized and locally targeted treatment plans for better treatment responsivity.
Concerti's results address the overwhelming challenges researches face when developing 396 therapeutics and may help facilitate the key to effective vaccine development.
Thailand Medical News takeaway, is that while the IBM research paper is basically to show the effectives of the Concerti algorithm, what was concerning is the findings that the research team uncovered ie co-infections and heteroplasmy in many COVID-19 patients.
More urgent studies are warranted to verify the frequency of heteroplasmy and what variants are at play constantly as the medical community urgently needs to get together and find ways to address these as a whole new approach to COVID-19 would be urgently needed.
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